| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: facebook/deit-small-patch16-224 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - imagefolder |
| metrics: |
| - accuracy |
| - recall |
| model-index: |
| - name: deit_fold_2_v3 |
| results: |
| - task: |
| name: Image Classification |
| type: image-classification |
| dataset: |
| name: imagefolder |
| type: imagefolder |
| config: default |
| split: None |
| args: default |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.9519230769230769 |
| - name: Recall |
| type: recall |
| value: 0.9579300074460163 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # deit_fold_2_v3 |
| |
| This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.1298 |
| - Accuracy: 0.9519 |
| - F1 Score: 0.9537 |
| - Recall: 0.9579 |
| |
| ## Model description |
| |
| More information needed |
| |
| ## Intended uses & limitations |
| |
| More information needed |
| |
| ## Training and evaluation data |
| |
| More information needed |
| |
| ## Training procedure |
| |
| ### Training hyperparameters |
| |
| The following hyperparameters were used during training: |
| - learning_rate: 1e-05 |
| - train_batch_size: 64 |
| - eval_batch_size: 64 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_steps: 150 |
| - num_epochs: 100 |
| - label_smoothing_factor: 0.15 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:| |
| | 2.8252 | 1.0 | 20 | 2.8294 | 0.2532 | 0.1757 | 0.2799 | |
| | 2.6170 | 2.0 | 40 | 2.6009 | 0.4231 | 0.3643 | 0.4176 | |
| | 2.3206 | 3.0 | 60 | 2.3003 | 0.6282 | 0.6023 | 0.6150 | |
| | 1.9316 | 4.0 | 80 | 1.9340 | 0.7468 | 0.7536 | 0.7418 | |
| | 1.6434 | 5.0 | 100 | 1.6591 | 0.8301 | 0.8350 | 0.8313 | |
| | 1.4153 | 6.0 | 120 | 1.4709 | 0.8718 | 0.8757 | 0.8768 | |
| | 1.2545 | 7.0 | 140 | 1.3896 | 0.8846 | 0.8842 | 0.8805 | |
| | 1.2233 | 8.0 | 160 | 1.3060 | 0.9006 | 0.9020 | 0.8985 | |
| | 1.1294 | 9.0 | 180 | 1.2383 | 0.9295 | 0.9305 | 0.9327 | |
| | 1.1183 | 10.0 | 200 | 1.2321 | 0.9167 | 0.9188 | 0.9187 | |
| | 1.1030 | 11.0 | 220 | 1.2141 | 0.9263 | 0.9277 | 0.9273 | |
| | 1.0461 | 12.0 | 240 | 1.1977 | 0.9327 | 0.9341 | 0.9346 | |
| | 1.0356 | 13.0 | 260 | 1.1742 | 0.9327 | 0.9345 | 0.9359 | |
| | 1.0387 | 14.0 | 280 | 1.1660 | 0.9295 | 0.9322 | 0.9366 | |
| | 1.0136 | 15.0 | 300 | 1.1681 | 0.9295 | 0.9318 | 0.9359 | |
| | 1.0312 | 16.0 | 320 | 1.1628 | 0.9295 | 0.9316 | 0.9346 | |
| | 1.0127 | 17.0 | 340 | 1.1365 | 0.9391 | 0.9409 | 0.9420 | |
| | 1.0043 | 18.0 | 360 | 1.1322 | 0.9423 | 0.9439 | 0.9444 | |
| | 0.9970 | 19.0 | 380 | 1.1653 | 0.9455 | 0.9473 | 0.9532 | |
| | 1.0022 | 20.0 | 400 | 1.1536 | 0.9359 | 0.9380 | 0.9415 | |
| | 1.0002 | 21.0 | 420 | 1.1603 | 0.9391 | 0.9412 | 0.9469 | |
| | 1.0062 | 22.0 | 440 | 1.1409 | 0.9455 | 0.9470 | 0.9518 | |
| | 0.9826 | 23.0 | 460 | 1.1523 | 0.9423 | 0.9437 | 0.9415 | |
| | 0.9858 | 24.0 | 480 | 1.1611 | 0.9455 | 0.9472 | 0.9513 | |
| | 0.9753 | 25.0 | 500 | 1.1412 | 0.9455 | 0.9472 | 0.9506 | |
| | 0.9730 | 26.0 | 520 | 1.1605 | 0.9423 | 0.9441 | 0.9427 | |
| | 0.9833 | 27.0 | 540 | 1.1281 | 0.9519 | 0.9531 | 0.9555 | |
| | 0.9765 | 28.0 | 560 | 1.1411 | 0.9487 | 0.9503 | 0.9501 | |
| | 0.9791 | 29.0 | 580 | 1.1365 | 0.9487 | 0.9511 | 0.9555 | |
| | 0.9748 | 30.0 | 600 | 1.1481 | 0.9391 | 0.9415 | 0.9450 | |
| | 0.9725 | 31.0 | 620 | 1.1567 | 0.9359 | 0.9370 | 0.9366 | |
| | 0.9645 | 32.0 | 640 | 1.1298 | 0.9519 | 0.9537 | 0.9579 | |
| | 0.9599 | 33.0 | 660 | 1.1275 | 0.9487 | 0.9500 | 0.9518 | |
| | 0.9701 | 34.0 | 680 | 1.1281 | 0.9519 | 0.9530 | 0.9543 | |
| | 0.9785 | 35.0 | 700 | 1.1293 | 0.9455 | 0.9472 | 0.9506 | |
| | 0.9714 | 36.0 | 720 | 1.1466 | 0.9455 | 0.9473 | 0.9513 | |
| | 0.9687 | 37.0 | 740 | 1.1502 | 0.9423 | 0.9447 | 0.9501 | |
| | 0.9581 | 38.0 | 760 | 1.1606 | 0.9391 | 0.9415 | 0.9464 | |
| | 0.9675 | 39.0 | 780 | 1.1502 | 0.9423 | 0.9442 | 0.9462 | |
| | 0.9697 | 40.0 | 800 | 1.1641 | 0.9391 | 0.9414 | 0.9476 | |
| | 0.9664 | 41.0 | 820 | 1.1442 | 0.9455 | 0.9475 | 0.9523 | |
| | 0.9567 | 42.0 | 840 | 1.1395 | 0.9423 | 0.9441 | 0.9462 | |
| | 0.9716 | 43.0 | 860 | 1.1537 | 0.9359 | 0.9380 | 0.9415 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.0.0 |
| - Pytorch 2.10.0+cu128 |
| - Datasets 5.0.0 |
| - Tokenizers 0.22.2 |
| |
